Colloquium Schedule Fall 2026

Understanding accretion onto compact objects and the outflows they produce requires modeling the coupled physics of gas, dust, radiation, magnetic fields, and energetic particles across a vast range of spatial and temporal scales. In this talk, I will review recent progress in modeling multi-frequency radiation transport and photoionization, with particular emphasis on line-driven winds, in which radiation pressure on spectral lines accelerates gas away from the central source. I will also discuss new efforts to use machine learning to couple detailed photoionization calculations with radiation-hydrodynamic simulations. Together, these developments bring us closer to connecting the evolving radiation field, ionization state, and gas dynamics self-consistently. Such predictive modeling will become increasingly important as a new generation of observatories—including NewAthena, the Habitable Worlds Observatory, Roman, thirty-meter-class telescopes, and IceCube-Gen2—provides unprecedented constraints on the structure, dynamics, and energetics of accretion flows and their outflows.

By spatially resolving faint planets from their bright host stars, we can directly image exoplanets and characterize them as individual worlds. Exoplanet imaging is limited by our ability to separate the signal of the planet from the bright glare of the star. I will discuss novel techniques that allow us to better detect and characterize Jovian exoplanets with existing telescopes, and if placed on the next generation of observatories, have the potential for us to study habitable worlds. First, I will present significant improvements to the sensitivity of JWST coronagraphs thanks to new data analysis algorithms that utilize techniques from computational imaging that better leverage our knowledge of physics. Next, I will present two techniques that are now enabling new exoplanet properties to be precisely measured at the population level: 1) combining high spatial and spectral resolution to spectrally resolve molecular absorption lines in the atmospheres of directly imaged planets using the Keck Planet Imager and Characterizer (KPIC) and 2) long-baseline optical interferometry with VLTI/GRAVITY to enable an order of magnitude better orbital determination. I will highlight recent science results from these instruments showcasing new insights on giant plant formation. Finally, I will present the status of the Roman Space Telescope Coronagraph Instrument, which will demonstrate high-order wavefront control in space, potentially image a planet in reflected light for the first time, and mature the technology needed for Habitable Worlds Observatory.

DFT is routinely used to simulate point defects in solids and calculate their formation energies as a function of chemical growth conditions, Fermi level, and defect charge. Defect energy plots help ascertain the donor- and acceptor-type nature of defects, their relative stabilities, their shallow or deep levels, the equilibrium Fermi level, and temperature-dependent defect concentrations [1-3]. The need for large supercells, charge states, and advanced functionals makes defect calculations very expensive, prohibiting their application to massive semiconductor-defect chemical spaces. Combining DFT with machine learning (ML) helps address the computational expense by enabling on-demand predictions of defect energetics and defect levels directly from descriptor-based or structure-based representations. In this seminar, I will discuss my group’s work in combining high-throughput DFT computations with fine-tuned “foundation” machine learning interatomic potential (MLIP) models [4] for understanding point defect behavior in a variety of chalcogenide and halide semiconductors [5-7]. We developed a computational workflow that uses both semi-local and hybrid functionals to generate datasets of native point defects, impurities, dopants, and defect complexes, also accounting for energy-lowering symmetry-broken configurations [8]. Foundation MLIPs systematically retrained and fine-tuned on this data subsequently enable prediction and optimization of thousands of new point defects and complexes, and identification of the lowest energy defects. This scheme was applied for rational discovery and screening of low energy defect structures in dozens of semiconductors belonging to: (a) Cd/Zn-Te/Se/S compositions, relevant for CdTe solar cells [5,7], (b) a variety of inorganic halide (e.g., CsPbI3) and chalcogenide (e.g., BaZrS3) perovskites [6,9], (c) zincblende-derived ternary and quaternary chalcogenides (e.g., Cu(In,Ga)S2 and Ag2ZnSnSe4) [10], and (d) doped oxide perovskites (e.g., CaHfO3 and SrHfO3) targeted for scintillator application.

References
[1] A. Mannodi-Kanakkithodi et al., Patterns. 3, 3, 100450 (2022).
[2] M.H. Rahman et al., J. Phys. Mater. 8 022001 (2025).
[3] A. Mannodi-Kanakkithodi et al., MRS Bulletin. 51, 600–614 (2026).
[4] J. Riebesell et al., Nature Machine Intelligence 7, 836–847 (2025).
[5] M.H. Rahman et al., APL Machine Learning. 2, 016122 (2024).
[6] M. Biswas et al., J. Chem. Inf. Model., 66 (3), 1353-1370 (2026).
[7] M.H. Rahman et al., Physical Chemistry Chemical Physics. 28, 10718 - 10730 (2026).
[8] I. Mosquera-Lois et al., npj Comput. Mater. 9, 25 (2023).
[9] R. Desai et al., Journal of Physical Chemistry C. 129 (16), 7967-7976 (2025).
[10] M.H. Rahman et al., EES Solar. 2 (4): 984 – 1000 (2026).

Speaker Bio: Arun Mannodi Kanakkithodi is an assistant professor in the School of Materials Engineering at Purdue University. He received a Bachelor of Technology in Metallurgical and Materials Engineering from IIT Roorkee in 2012 and a PhD in Materials Science and Engineering from the University of Connecticut in 2017. He worked as a postdoctoral researcher at Argonne National Laboratory from 2017 till 2020. Arun’s research primarily involves applying first principles simulations and methods rooted in data science and machine learning for materials design. He is a contributor to and co-organizer of machine learning resources and hands-on workshops for nanoHUB, and a regular organizer of materials informatics tutorials at Materials Research Society (MRS) fall and spring meetings as part of the MRS AI Topical Community. Arun is a recipient of the 2020 Distinguished Young Investigator award from Argonne, the 2023 Functional Materials Division (FMD) Young Leaders Professional Development Award from TMS, a 2023 DOE Solar Energy Technology Office (SETO) Small Innovation Projects in Solar (SIPS) awardee, a 2024 ACS Materials Au Rising Star in Materials Science, a 2025 DARPA Young Faculty Award, and a 2026 ISMM Early Career Award.